More than Vectors: How Multi-Faceted AI Databases Enable Smart Applications

Innovative companies are investing in AI to increase efficiency and gain a competitive edge. Their initiatives are often multi-faceted, with generative AI complementing outputs from natural language processing and predictive machine learning models. While vector databases are considered the key to AI, vectors are only part of the puzzle. A new type of database is emerging: the AI database.

AI databases support three primary types of AI models: machine learning, natural language processing, and GenAI.

• Machine learning. ML uses techniques like classification and clustering to find patterns in historical data, predict events, identify anomalies, and recommend actions.
• Natural language processing. NLP interprets and creates speech or text to assist tasks such as translation, sentiment analysis, or document summarization. These models primarily consume text files.
• GenAI. GenAI language models generate text, imagery, audio, or video based on what they learn from a corpus of existing content.
  

This research note, from BARC, explores the emergence of versatile AI databases that support multi-model applications. Practitioners, data/AI leaders, and business leaders should read this report to understand this new platform option for supporting modern AI/ML initiatives.